Project ID: 18
Project Topic and Exploration Questions
For our project, we are examining COVID deaths in the US Midwest region (Indiana, Ohio, Michigan, Wisconsin, Illinois, Minnesota) during 2020. With this data, we are looking at how different age and racial groups were affected disparately by the disease. Similarly, we will incorporate information regarding lockdowns and reopenings to understand how government reaction to COVID affected COVID deaths.
Question 1: What age group had the highest death rate?
Question 2: What age group had the lowest death rate?
Question 3: What states were hit hardest/least hard by COVID?
Question 4: What races in the US were hit hardest/least hard by COVID?
Question 5: Is there a relationship between income and potential to die from COVID?
Question 6: Were wealthier/less wealthy people affected by COVID differently than by other diseases?
We should probably add more questions that are more targeted at OUR specific project and the way it has gone.
Data Sources
Insert the CSVs and any website info we may use about government policies.
Data Journey and Caveats
At the beginning of this project, we intended to include data regarding the entire United States. However, when looking at how large the dataset was and how unmanageable this data was (Python could not process the initial, entire CSV), we decided to focus particularly on the Midwest. We chose the Midwest not only because the states have a diversity in political orientation, but also because it is applicable to us at Notre Dame in Indiana. After speaking with Professor Kumar about how to refine our data and focus our project, we decided to orient our analysis to the ramifications of political policy on COVID deaths.
Because of the limited scope of our dataset, there are a few caveats that must be considered. Firstly, because we are only looking at the Midwest, this data may be correlated to the general trend of the US, but it cannot be taken as representative of the entire country. Similarly, there are a multitide of factors that we would have liked to focus more on (like income, access to healthcare, and geographical location within the state), but because of the size of the project, we could not incorporate this data. Thus, when considering the insights of the project, remember that there may be alternative factors that we have not fully taken into account.
About the Project Members
Will Mansour
I am a sophomore history major from Dallas, TX. I focus my historical research and courseload on issues regarding slavery, race, and the economy in the 19th century US. In my free time, I like to watch and play sports, exercise, and watch movies with my friends. This project interests me because I am passionate about public policy and how to lessen the negative effects of disease.
Zach Cali
I am a current sophomore and resident of Keenan Hall, studying architecture. I am from Hasbrouck Heights, NJ, a suburb of New York City. In my leisure time, I like to play my guitar, run, watch movies and tv shows with my friends, as well as draw. As I hail from NJ, my state was hit very hard at the beginning of the pandemic because of how densely populated it is. As I know many friends and family members impacted by the pandemic, I am very interested to see how the virus affected other parts of the country.
Visualizations
Visualizations 1 and 2: Insights
Generally speaking, and as expected, the older age brackets were much more likely to fluctuate as a result of their age complications impacting their overall health. Through the animation we can tell that there was an uptick in Covid Deaths around week 13 or week 14 of 2020 which corresponds to the middle of March, which is when everything shut down. At the end of the year, many of the ethnic groups of the age group in 50-64 actually had higher Covid Deaths than their 85 and older counterparts which defies the age trend you would expect to see and had seen up until that point. Perhaps the variant of the end of 2020 was more deadly to middle-aged folk.
Visualization 3: Insights
*Before beginning, it must be understood that the deaths counted in this visual are deaths from solely COVID-19, in other words, not those with other health complications.* Ohio has the highest Covid-19 death rate per 100,000 people of the midwestern states. Illinois and Wisconsin have the closest death rates per 100,000 people of the midwestern states. Minnesota has the lowest Covid death rate per 100,000 people of the midwestern states. There is a general consensus and stereotype, whether it is based in fact or not, that Democratic politicans are more hard on COVID and enforce COVID policy more strictly than Republican politicians. However, when looking at the governors for these Midwestern states, there seems to be a correlation between gubernatorial party and COVID deaths per capita, with states having a republican governor suffering higher death rates than those with a democratic governor. Of course, this is simply a correlation and there are several other factors, specifically the actual policies enforced to combat COVID (which will be mentioned later), but it is insightful when understanding why certain stereotypes arise regarding certain parties and their treatment of COVID-19.''')
Visualization 4: Insights
Ohio has not only the most deaths per capita as shown in the previous visualization, but also the highest raw total of the six states. It has more deaths than Illinois which has a larger population. Wisconsin and Minnesota both fall towards the bottom in raw total deaths.